Classification of peripheral blood cell images using deep learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.

Detalhes bibliográficos
Principais autores:	Aadi, Oyshik Ahmed, Akash, Md.Meghdad Hossain, Ishraq, Fahim, Hossain, Asif, Al Fahim, Abdullah
Outros Autores:	Karim, Dewan Ziaul
Formato:	Tese
Idioma:	English
Publicado em:	Brac University 2024
Assuntos:	Convolutional neural network Classification of blood cells Deep learning Cognitive learning theory (Deep learning)
Acesso em linha:	http://hdl.handle.net/10361/22062

id	10361-22062
record_format	dspace
spelling	10361-220622024-01-03T21:02:35Z Classification of peripheral blood cell images using deep learning Aadi, Oyshik Ahmed Akash, Md.Meghdad Hossain Ishraq, Fahim Hossain, Asif Al Fahim, Abdullah Karim, Dewan Ziaul Department of Computer Science and Engineering, Brac University Convolutional neural network Classification of blood cells Deep learning Cognitive learning theory (Deep learning) This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023. Cataloged from PDF version of thesis. Includes bibliographical references (pages 36-37). Diagnosis and Identification of cells and disease infected cells are and important part of in medical science that bears huge significance even today. There are health implications can often be identified my observing the morphological changes of cells as well as the quantity of cells. The traditional methods of counting blood and chemically identifying diseases can be expensive and/or time consuming to the extent that only certain medical centres can perform the task at hand, or take days to receive a report of. However, we believe Deep Learning with Convolutional Neural Networks (CNNs) can take over most of this tedious process. In this work, we aim towards creating a custom CNN model that can quickly classify different kinds of peripheral blood cells such as the 7 different white blood cell types (basophils, erythroblasts, ig, eosinophils, lymphocytes, monocytes, neutrophils) and platelets. Such a model can be used in blood cell counts which can be used to identify cases like leukemia. Moreover, such a method can be extended into other fields such as red blood cell detection or even infected cell detection, which includes identifying diseases from Sickle Cell Anemia to cells affected by Covid19. Our custom CNN model has performed exceptionally well, achieving accuracies as high as 99.1% and 98.9% in training and validation respectively, which is significantly higher than using pre-trained models such as DenseNet or NasNet. In more ways than one, we show how our model is better suited for the task at hand. Oyshik Ahmed Aadi Md.Meghdad Hossain Akash Fahim Ishraq Asif Hossain Abdullah Al Fahim B.Sc. in Computer Science 2024-01-03T08:34:41Z 2024-01-03T08:34:41Z 2023 2023-01 Thesis ID: 18201052 ID: 19201138 ID: 18301077 ID: 22241039 ID: 18201099 http://hdl.handle.net/10361/22062 en Brac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. 37 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Convolutional neural network Classification of blood cells Deep learning Cognitive learning theory (Deep learning)
spellingShingle	Convolutional neural network Classification of blood cells Deep learning Cognitive learning theory (Deep learning) Aadi, Oyshik Ahmed Akash, Md.Meghdad Hossain Ishraq, Fahim Hossain, Asif Al Fahim, Abdullah Classification of peripheral blood cell images using deep learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2023.
author2	Karim, Dewan Ziaul
author_facet	Karim, Dewan Ziaul Aadi, Oyshik Ahmed Akash, Md.Meghdad Hossain Ishraq, Fahim Hossain, Asif Al Fahim, Abdullah
format	Thesis
author	Aadi, Oyshik Ahmed Akash, Md.Meghdad Hossain Ishraq, Fahim Hossain, Asif Al Fahim, Abdullah
author_sort	Aadi, Oyshik Ahmed
title	Classification of peripheral blood cell images using deep learning
title_short	Classification of peripheral blood cell images using deep learning
title_full	Classification of peripheral blood cell images using deep learning
title_fullStr	Classification of peripheral blood cell images using deep learning
title_full_unstemmed	Classification of peripheral blood cell images using deep learning
title_sort	classification of peripheral blood cell images using deep learning
publisher	Brac University
publishDate	2024
url	http://hdl.handle.net/10361/22062
work_keys_str_mv	AT aadioyshikahmed classificationofperipheralbloodcellimagesusingdeeplearning AT akashmdmeghdadhossain classificationofperipheralbloodcellimagesusingdeeplearning AT ishraqfahim classificationofperipheralbloodcellimagesusingdeeplearning AT hossainasif classificationofperipheralbloodcellimagesusingdeeplearning AT alfahimabdullah classificationofperipheralbloodcellimagesusingdeeplearning
_version_	1814308339402670080

Classification of peripheral blood cell images using deep learning

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